Method for monitoring manufacture of assembly units

ABSTRACT

One variation of a method for monitoring manufacture of assembly units includes: receiving selection of a target location hypothesized by a user to contain an origin of a defect in assembly units of an assembly type; accessing a feature map linking non-visual manufacturing features to physical locations within the assembly type; for each assembly unit, accessing an inspection image of the assembly unit recorded by an optical inspection station during production of the assembly unit, projecting the target location onto the inspection image, detecting visual features proximal the target location within the inspection image, and aggregating non-visual manufacturing features associated with locations proximal the target location and representing manufacturing inputs into the assembly unit based on the feature map; and calculating correlations between visual and non-visual manufacturing features associated with locations proximal the target location and the defect for the set of assembly units.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Application is a continuation of U.S. patent application Ser. No.17/461,773, filed on 30 Aug. 2021, which is a continuation of U.S.patent application Ser. No. 16/506,905, filed on 9 Jul. 2019, whichclaims the benefit of U.S. Provisional Application No. 62/695,727, filedon 9 Jul. 2018, each of which is incorporated in its entirety by thisreference.

This Application is related to U.S. patent application Ser. Nos.15/407,158, 15/407,162, 15/653,040, and 15/953,206, each of which isincorporated in its entirety by this reference.

TECHNICAL FIELD

This invention relates generally to the field of optical inspection andmore specifically to a new and useful method for monitoring manufactureof assembly units in the field of manufacturing.

BRIEF DESCRIPTION OF THE FIGS.

FIG. 1 is a flowchart representation of a method;

FIG. 2 is a graphical representation of one variation of the method;

FIG. 3 is a graphical representation of one variation of the method;

FIG. 4 is a graphical representation of one variation of the method; and

FIGS. 5A and 5B are flowchart representation of one variation of themethod.

DESCRIPTION OF THE EMBODIMENTS

The following description of embodiments of the invention is notintended to limit the invention to these embodiments but rather toenable a person skilled in the art to make and use this invention.Variations, configurations, implementations, example implementations,and examples described herein are optional and are not exclusive to thevariations, configurations, implementations, example implementations,and examples they describe. The invention described herein can includeany and all permutations of these variations, configurations,implementations, example implementations, and examples.

1. Methods

As shown in FIGS. 1-4, a method S100 for monitoring manufacture ofassembly units includes: accessing a set of inspection images of a setof assembly units, of a particular assembly type, recorded by opticalinspection stations during production of the set of assembly units inBlock S110; for each inspection image in the set of inspection images,detecting a set of visual features in the inspection image in BlockS120; aggregating a set of non-visual manufacturing featuresrepresenting a set of manufacturing inputs to the particular assemblytype during production on an assembly line in Block S130; receivingindication of a defect identified in a subset of assembly units in theset of assembly units in Block S140; receiving selection of a particularlocation, for the particular assembly type, hypothesized by a user tocontain an origin of the defect in Block S142; calculating weights ofvisual features in the set of visual features proportional to spatialproximity to the particular location in Block S150; based on a featuremap linking the set of manufacturing inputs to regions of the particularassembly unit type, calculating weights of manufacturing inputs in theset of manufacturing inputs based on temporal proximity and spatialproximity to a set of manufacturing steps effecting the particularlocation for the particular assembly type in Block S150; calculatingcorrelations between a subset of visual features in the set of visualfeatures, a subset of non-visual manufacturing features in the set ofnon-visual manufacturing features, and presence of the defect across theset of assembly units based on weights of visual features in the set ofvisual features and weights of manufacturing inputs in the set ofmanufacturing inputs in Block S152; isolating a particular manufacturinginput, in the set of manufacturing inputs, linked to visual features andnon-visual manufacturing features exhibiting greatest correlation to thedefect in Block S154; and outputting a prompt to inspect the particularmanufacturing input in Block S16o.

One variation of the method S100 shown in FIGS. 5A and 5B includes:receiving indication of a defect identified in a subset of assemblyunits in a set of assembly units of a particular assembly type in BlockS140; identifying a target location, for the particular assembly type,hypothesized to contain an origin of the defect in Block S142; accessinga set of non-visual manufacturing features representing a set ofmanufacturing inputs into the set of assembly units during production ofthe set of assembly units in Block S130; and accessing a feature maplinking the set of non-visual manufacturing features to locations withinassembly units of the particular assembly type in Block S150. Thisvariation of the method S100 also includes, for each assembly unit inthe set of assembly units: accessing an inspection image, in a set ofinspection images, depicting the assembly unit and recorded by anoptical inspection station during production of the assembly unit inBlock S110; projecting the target location onto the inspection image inBlock S120; detecting a set of visual features proximal the targetlocation in the inspection image in Block S120; and, based on thefeature map, aggregating a subset of non-visual manufacturing features,in the set of non-visual manufacturing features, associated withlocations proximal the target location in Block S150. This variation ofthe method S100 further includes: calculating correlations between setsof visual features proximal the target location, subsets of non-visualmanufacturing features associated with locations proximal the targetlocation, and the defect for the set of assembly units in Block S152;isolating a particular non-visual manufacturing feature, in the set ofnon-visual manufacturing features, exhibiting greatest correlation tothe defect in Block S154; and generating a prompt to inspect a source ofthe particular non-visual manufacturing feature for the particularassembly type in Block S160.

As shown in FIGS. 5A and 5B, the foregoing variation of the method S100can additionally or alternatively include, for each assembly unit in theset of assembly units: accessing an inspection image, in a set ofinspection images, depicting the assembly unit and recorded by anoptical inspection station during production of the assembly unit inBlock S110; projecting the target location onto the inspection image inBlock S120; detecting a set of visual features proximal the targetlocation in the inspection image in Block S120; extracting a cluster ofnon-visual manufacturing features, in the set of non-visualmanufacturing features, associated with times proximal a timestamp ofthe inspection image in Block S150; and, based on the feature map,aggregating a subset of non-visual manufacturing features, associatedwith locations proximal the target location for the assembly unit andassociated with times proximal a timestamp of the inspection image, fromthe set of non-visual manufacturing features in Block S150.

2. Applications

Generally, the computer system can: leverage optical data of assemblyunits recorded during assembly to link structured and unstructuredmanufacturing data and assembly unit outcome data to these assemblyunits in both time and space; derive multi-dimensional correlationsbetween visual features, non-visual features, and outcomes in assemblyunits based on temporal and physical proximity; and then outputinsights, guidance, or prompts to reduce defects and improve outcomes infuture assembly units. In particular, the computer system can implementBlocks of the method S100 to anchor manufacturing data and assembly unitoutcome data (e.g., defect vectors) to discrete locations in inspectionimages of assembly units recorded at discrete times during assembly ofthese assembly units. The computer system can then derive correlationsbetween manufacturing inputs (i.e., non-visual features, such as toolsettings, station operators, component batch numbers), physical featuresinstantiated in assembly units in one or more stages of assembly anddetectable (i.e., as visual features) in inspection images of theseassembly units, and outcomes (e.g., defects, test results, inspectionresults) of these assembly units based on temporal and spatial proximityof these visual and non-visual features to detected or hypothesizedorigins of these defects in this population of assembly units.

The computer system can execute Blocks of the method S100 to develop a“feature map” (e.g., in the form of a multi-dimensional matrix for aparticular assembly line) that links manufacturing inputs (e.g., controlinputs, measurements, test results) to physical regions of assemblyunits at various assembly stages—depicted in inspection images recordedalong the assembly line—including in both space and time. In particular,the feature map can store: physical locations at which manufacturinginputs effect or modify a particular point, line, area, or volumelocation on assembly units of this assembly type; relative times thatthese manufacturing inputs are applied to these assembly units of thisassembly type during a sequence of manufacturing steps and assemblystages; and/or relative times that these assembly units are exposed tothese manufacturing input during this sequence of manufacturing stepsand assembly stages s. For example, during or after production of afirst assembly unit at an assembly line, the computer system can ingesttimeseries and/or georeferenced) manufacturing data of different typesfor this assembly unit, such as: timestamped ambient data; assemblytechnician and station operator identifiers; component supplier andbatch identifiers; component test data; screw driver torques; adhesivetypes and application conditions; finishing processes; assembly order;line equipment settings and timestamped use data; etc. Thesemanufacturing data can include: binary values (e.g., “yes” or “no”values representing whether an antenna was installed on the assemblyunit, “pass” or “fail” values for a test result of an antenna installedon the assembly unit); higher-resolution numerical measurements (e.g.,antenna length in millimeters); and/or vectors or spectral values (e.g.,a timeseries or spectral response, such as signal strength in decibelsover a frequency range). The computer system can also ingest timestampedinspection images (e.g., 2D color photographic images; IR, UV, X-ray, ormulti-spectral images; 3D CT or stereoscopic images) of this assemblyunit recorded by optical inspection stations along the assembly line.

When the assembly unit is tested—such as during or after assembly—and adefect in the assembly unit subsequently identified, the user may entera type, scope, magnitude, or other description of this defect for theassembly unit via a user portal. The computer system can ingest thisdefect description and prompt the user to enter a hypothesis for anorigin of this defect, such as: a physical location of a particularcomponent on the assembly unit that the user predicts caused the defect(e.g., due to failure of the particular component); a physical locationproximal a cluster of components the user predicts yielded the defect(e.g., due to misalignment of these components); or a relative time orassembly step during manufacture of the assembly unit in which the userpredicts occurrence of an error or exposure that yielded the defect. Thecomputer system can also prompt the user to enter multiple hypothesesfor spatial or temporal origins of the defect and then link thesespatial or temporal hypotheses to the defect.

For example, for a spatial hypothesis for the defect, the computersystem can: link the defect to a region of interest around ahypothesized spatial origin of the defect; and extract visual featuresfrom this region of interest depicted in an inspection image of theassembly unit recorded by an optical inspection station along theassembly line. The computer system can also: collate manufacturinginputs (e.g., assembly steps) associated with changes on the assemblyunit near this region of interest based on the feature map for thisassembly type; retrieve timestamped non-visual manufacturing datadescriptive of these manufacturing inputs when applied to the assemblyunit to effect the region of interest (e.g., tool settings, componentsbatch numbers, and station operators recorded at times proximal atimestamp of the inspection image immediately preceding a process in theregion of interest); and retrieve timestamped non-visual manufacturingdata descriptive of other conditions exposed to, applied to, orotherwise affecting the assembly unit—according to the feature map—attimes approximately concurrent these changes near the region of interestin the assembly unit.

In another example, for a temporal hypothesis for the defect, thecomputer system can: identify a set of manufacturing inputs (e.g.,assembly steps) applied to the assembly unit near the hypothesized timeof the defect origin based on the feature map; aggregate points, lines,areas, or volumes on the assembly unit associated with this set ofmanufacturing inputs based on the feature map; project these points,lines, areas, or volumes onto an inspection image of the assembly unitrecorded near (and immediately after) the hypothesized time of thedefect origin to define a set of regions of interest; and extract visualfeatures from these regions of interest depicted in the inspectionimage. The computer system can also: retrieve timestamped non-visualmanufacturing data descriptive of these manufacturing inputs whenapplied to the assembly unit to effect the region of interest (e.g.,tool settings, components batch numbers, and station operators recordedat times proximal a timestamp of the inspection image immediatelypreceding a process in the region of interest); and retrieve timestampednon-visual manufacturing data descriptive of other conditions exposedto, applied to, or otherwise affecting the assembly unit—according tothe feature map—at times approximately concurrent these changes near theregion of interest in the assembly unit.

In the foregoing examples, the computer system can then compile thesevisual features and non-visual features into a container (e.g., avector) and repeat this process to generate similar containers for otherassembly units of the same assembly type by projecting this region ofinterest onto inspection images of these other assembly units andaggregating visual and non-visual features for these other assemblyunits according to the region of interest and the feature map. Thecomputer system can then implement artificial intelligence, machinelearning (e.g., with embeddings), regression, statistical analysis,and/or other methods and techniques to quantify correlations betweenthese features and the defect.

The computer system can thus execute Blocks of the method S100 to:develop a contextual understanding of relationships betweenmanufacturing inputs and physical features in assembly units of aparticular assembly type; implement this contextual understanding tofilter a large set of visual assembly unit features and non-visualprocess-related features down to a small number of features spatiallyand temporally nearest—and therefore most likely to effect—a defect inan assembly unit of this type; and to converge on an even smaller numberof target visual and/or non-visual features exhibiting greatestcorrelation (or covariance, or probability of causation) to this defect.

The computer system can then present these target features to a user(e.g., a manufacturing engineer, a line technician or operator), such asby: overlaying colored markers with defect correlation values (e.g.,from 0.00 to 1.00) over corresponding visual target features in aninspection image of an assembly unit of this type; populating aninvestigation spreadsheet with descriptors of non-visual target features(e.g., a tool identifier and description, an operator ID, an assemblystage identifier) and their corresponding defect correlation values; andthen serving this annotated inspection image and investigationspreadsheet to the user, such as through a user portal within a nativeapplication or accessed via a web browser executing on the user'scomputing device. The user may then sequentially investigate—remotely orin-person at the assembly line—these target features, such as in orderof their defect correlation values.

Therefore, rather than scan all available (e.g., thousands of, millionsof) visual and non-visual features representative of an assembly unitfor strength of correlation to a defect, the computer system can insteadexecute Blocks of the method S100 to focus derivation of strength ofcorrelation to a defect for a small subset of visual and non-visualfeatures of an assembly unit that are spatially and temporally proximala defect origin hypothesized by the user. Thus, the computer system canexecute Blocks of the method S100 to rapidly and accurately isolate asmall number of visual and/or non-visual features that exhibit strongestcorrelation to a defect—despite quantity of imaged assembly units (e.g.,as few as ten or as many millions of imaged assembly units) and withminimal computational load. The computer system can further executeBlocks of the method S100 to articulate these correlations to a user inorder to guide manufacturing-related investigations into origins of thedefect, thereby enabling the user to rapidly isolate an origin of adefect—despite the quantity of imaged assembly units—and to correct thisorigin to reduce frequency of this defect.

3. System

Blocks of the method S100 can be executed by a computer system, such as:locally on an optical inspection station (as described below) at whichinspection images of assembly units are recorded; locally near anassembly line populated with optical inspection stations; within amanufacturing space or manufacturing center occupied by this assemblyline; or remotely at a remote server connected to optical inspectionstations via a computer network (e.g., the Internet), etc. The computersystem can also interface directly with other sensors arranged along ornear the assembly line to collect non-visual manufacturing and test dataor retrieve these data from a report database associated with theassembly. Furthermore, the computer system can interface with databasescontaining other non-visual manufacturing data for assembly unitsproduced on this assembly line, such as: test data for batches ofcomponents supplied to the assembly line; supplier, manufacturer, andproduction data for components supplied to the assembly line; etc.

The computer system can also interface with a user (e.g., an engineer,an assembly line worker) via a user portal—such as accessible through aweb browser or native application executing on a laptop computer orsmartphone—to serve prompts and notifications to the user and to receivedefect labels, anomaly feedback, or other supervision from the user.

The method S100 is described below as executed by the computer system:to map a relationship between visual and non-visual features for anassembly type in time and space; to leverage these relationships toderive correlations between defects detected in assembly units of thistype and visual/non-visual data collected during production of theseassembly units; and to leverage these relationships to correlate visualanomalies in assembly units to non-visual root causes (and vice versa)based on visual and non-visual data collected during production of theseassembly units. However, the method S100 can be similarly implemented bythe computer system to derive correlations between visual/non-visualfeatures and anomalies/defects in singular parts (e.g., molded, cast,stamped, or machined parts) based on inspection image and non-visualmanufacturing data generated during production of these singular parts.

4. Optical Inspection Station and Inspection Images

As shown in FIG. 1, the computer system accesses inspection imagesrecorded by an optical inspection station during assembly of assemblyunits in Block S110. For example, the computer system can retrieveinspection images recorded by an optical inspection station, uploadedfrom the optical inspection station to a file system (e.g., a database)via a computer network, and stored in a database. The computer systemcan additionally or alternatively retrieve inspection images directlyfrom the optical inspection station, such as in real-time when aninspection image of an assembly unit is recorded by the opticalinspection station.

As described in U.S. patent application Ser. No. 15/653,040, an opticalinspection station can include: an imaging platform that receives a partor assembly; a visible light camera (e.g., a RGB CMOS, or black andwhite CCD camera) that captures inspection images (e.g., digitalphotographic color images) of units placed on the imaging platform; anda data bus that offloads inspection images, such as to a local or remotedatabase. An optical inspection station can additionally oralternatively include multiple visible light cameras, one or moreinfrared cameras, a laser depth sensor, etc.

In one implementation, an optical inspection station also includes adepth camera, such as an infrared depth camera, configured to outputdepth images. In this implementation, the optical inspection station cantrigger both the visible light camera and the depth camera to capture acolor image and a depth image, respectively, of each unit placed on theimaging platform. Alternatively, the optical inspection station caninclude optical fiducials arranged on and/or near the imaging platform.In this implementation, the optical inspection station (or a local orremote computer system interfacing with the remote database) canimplement machine vision techniques to identify these fiducials in acolor image captured by the visible light camera and to transform sizes,geometries (e.g., distortions from known geometries), and/or positionsof these fiducials within the color image into a depth map, into athree-dimensional color image, or into a three-dimensional measurementspace (described below) for the color image, such as by passing thecolor image into a neural network.

Upon receipt or retrieval of an inspection image, the computer systemcan “dewarp,” “flatten,” or otherwise preprocess the inspection image inBlock S110 in preparation for detecting and extracting features from theinspection image in Block S120, as described in U.S. patent applicationSer. No. 15/407,158. The computer system can also: implement computervision techniques (e.g., object recognition, edge detection) to identifya perimeter or boundary of the assembly unit in the inspection image;and then crop the inspection image around the assembly unit such thatonly features corresponding to the assembly unit are extracted from theinspection image and processed in Block S120 of the method S100.

The computer system can thus aggregate a set of (e.g., 100, 1,000, or100,000) inspection images (e.g., digital color photographic image)recorded over a period of operation of an assembly line in Block S110,wherein each inspection image records visual characteristics of a uniqueassembly unit at a particular production stage. However, the computersystem can access inspection images of any other type and in any otherway in Block S110.

5. Visual Feature Extraction

Block S120 of the method S100 recites, for each inspection image in theset of inspection images, detecting a set of features in the inspectionimage. Generally, in Block S120, the computer system identifies multiple(e.g., “n,” or “many”) features representative of an assembly unitdepicted in an inspection image, characterizes these features, andaggregates these features into a multi-dimensional (e.g.,“n-dimensional”) vector or other container uniquely representing thisassembly unit.

In one implementation, the computer system implements a featureclassifier that defines: types of single-order features (e.g., corners,edges, areas, gradients); types of second-order features constructedfrom multiple single-order features (e.g., edge orientation and gradientmagnitude of an edge, polarity and strength of a blob); metrics forrelative positions and orientations of multiple features; and/orprioritization for detecting and extracting features from an inspectionimage. The computer system can then apply this feature classifier to thefull height and width of a region of the inspection image representingthe assembly unit. For example, the computer system can implementlow-level computer vision techniques (e.g., edge detection, ridgedetection), curvature-based computer vision techniques (e.g., changingintensity, autocorrelation), and/or shape-based computer visiontechniques (e.g., thresholding, blob extraction, templatematching)—according to the feature classifier—to detect n-number ofhighest-priority features representing the assembly unit in theinspection image in Block S120.

The computer system can then extract a local image patch around thesefeatures in Block S120, such as in the form of a multi-dimensional(e.g., n-dimensional) feature vector (hereinafter a “vector”)representing a corpus (e.g., thousands, millions) of features extractedfrom the inspection image. For example, this vector can define a“fingerprint” that uniquely represents visual features present on theassembly unit and depicted in this particular inspection image.

The computer system can repeat this process for other inspectionimages—such as by processing these inspection images in a batch or byprocessing new inspection images individually upon receipt from anoptical inspection station—to generate a population of vectors uniquelyrepresenting each assembly unit in this population of imaged assemblyunits.

6. Manufacturing Inputs

Block S130 of the method S100 recites aggregating non-visualmanufacturing data representing a set of manufacturing inputs andconditions along the assembly line during production of the set ofassembly units. Generally, in Block S130, the computer system collectsother manufacturing related data for assembly units manufactured alongthe assembly line, including both control inputs and measurement outputs(hereinafter “manufacturing data”), as shown in FIG. 1. For example,control inputs can include: inputs into the assembly line ormanufacturing process, such as equipment settings, tool paths, and/orwork instructions (e.g., a torque setting for an electronic screwdrivermanipulated manually by a technician or operator); component sources; aassembly station technician identifier; etc. Measurement outputs caninclude: unit-specific sensor data; ambient sensor data; actual assemblyequipment process data (e.g., the actual torque measured by anelectronic screwdriver during installation of a screw into a particularassembly unit); etc. (Measurement outputs can also include featurevectors generated from features detected in inspection images in BlockS120 described above.) “Manufacturing inputs” and “manufacturing data”can therefore include both “hard inputs” and measurement and test resultdata representing a stack of physical and functional relationshipsbetween components and modules combined to form assembly unit; asdescribed below, the computer system can also access assembly unit“outcome data,” such as indicating presence of absence of specificfunctional or aesthetic defects in assembly units and execute Blocks ofthe method S100 to derive correlations between these manufacturinginputs (i.e., hard inputs and measurement and test result data) andassembly unit outcomes (i.e., presence of absence of specific functionalor aesthetic defects).

For example, the computer system can interface with: ambient sensors tocollect temperature and humidity data near the assembly line; scales tocollect assembly unit weights at particular stages of assembly; part orassembly test rigs to collect assembly unit test results, such asgenerated by antenna test rigs, touch sensor test and calibration rigs,or environmental test rigs; assembly tools, such as a screwdriver tocollect screwdriver torque and dwell time values at a particularassembly stage; fixture and jig data, such as to collect an assemblyforce, weight distribution, or component presence report generated bysensors integrated into an assembly jig; robotic assembly systems, suchas tool paths or log files of a robotic arm or other roboticmanipulator—located along the assembly line—during installation of apart onto an assembly; etc. In this example, the user may link thecomputer system to these sensors and actuators directly, and thecomputer system can ingest these data in real-time. Alternatively, theuser may link the computer system to the database containing thesemanufacturing data, and the computer system can ingest these dataasynchronously.

The computer system can also access: upstream IQC data for parts andsubassemblies supplied to the assembly line; dimensional data and testdata for these supplied parts and subassemblies; 2D or 3D CAD models ordrawings of parts and subassemblies in the assembly type; dimension,tolerance, and material specifications for these parts andsubassemblies; cosmetic templates for the assembly type; data fromrobotic assembly equipment, CNC tools, injection-molding equipment, andother manufacturing equipment; work instructions or standard operatingprocedures (e.g., for humans) at assembly stations along the assemblyline; etc.

Furthermore, the computer system can access an assembly specificationfor this assembly type, such as: an order of assembly of individualcomponents; assembly steps and processes; assembly tools, jigs, andfixtures and related specifications; robotic assembly rigs and relatedprocesses and tool paths; adhesive types and specifications; etc. forthe assembly type.

However, the computer system can implement any other method andtechnique to ingest structured, unstructured, and/or semi-structuredmanufacturing data in any other format and related to parts andsubassemblies supplied to the assembly line, related to assembly ofthese parts and subassemblies, etc.

7. Feature Map

The computer system can also interface with the user through the userportal to develop a feature map linking manufacturing inputs andinspection images recorded by optical inspection stations along theassembly line in both time and place, as shown in FIGS. 1 and 2.

In one implementation, sensors, tools, robotic systems, productionequipment, assembly stations, etc. in or near the assembly line canrecord manufacturing-related data while the assembly line is inoperation, such as in the form of timestamped data streams including: 1Hz timeseries ambient humidity data; timestamped peak torque androtation count of discrete screwdriver operations; timestamped instancesof canned cycles of a robotic manipulator (e.g., a “robotic arm”) andrelated errors and peak loads; timestamped instances of heat stakeequipment cycles and corresponding tool temperatures; station operatorclock-in and clock-out times; etc. An optical inspection station—locatedin or near the assembly line—can similarly timestamp inspection imagesof assembly units placed in the optical inspection station.

In this implementation, the computer system can interface with the uservia the user portal to record links between these non-visualmanufacturing-related data streams and regions of inspection images ofassembly units of this assembly type. For example, the user mayinterface with the computer system via the user portal to: trigger thecomputer system to initialize a new inspection process for this assemblytype and/or assembly line; link this new inspection process to adatabase of existing inspection images of assembly units previouslyassembled on the assembly line; and/or link this new inspection processdirectly to optical inspection stations currently deployed on thisassembly line. The computer system can also interpret a series ofmanufacturing steps (or processes, assembly stations) along the assemblyline from production documents uploaded by the user (e.g., byimplementing natural language processing to extract manufacturing stepdescriptions from these production documents) or record manufacturingsteps entered manually by the user and then import these manufacturingstep definitions into the new inspection process for the assembly type.

The user may then link a subset of manufacturing steps for the assemblytype to a particular subregion of the assembly type at a particularassembly stage. For example, the computer system can retrieve a firstinspection image of a representative assembly unit of this assembly typeat a first stage of assembly and present this first inspection image tothe user via the user portal. The user may then: select a firstmanufacturing step definition—from this set imported into this newinspection process; and draw a bounding box around a component orsubassembly depicted in the first inspection image of the representativeassembly unit, select a particular object (e.g., a screw, a PCB, ahousing, a display), or select a particular feature (e.g., an edge, asurface) depicted in the first inspection image. The computer system canthen record a pointer between this first manufacturing step and thisbounding box, object, or feature for this first assembly stage of thisassembly type. The computer system can interface with the user to repeatthis process for each other manufacturing step thus defined for theassembly type.

The computer system can similarly interface with the user to link,connect, or otherwise define relationships between specific data streams(e.g., from sensors and actuators along the assembly line), actuator andoperator log files, and/or other non-visual manufacturing-related datarelated to operation of the assembly line. For example, the user maydefine a bounding box encompassing the entirety of a representativeassembly unit depicted in an inspection image and link this bounding boxto ambient temperature and humidity data streams recorded byenvironmental sensors proximal an assembly station immediately precedingan optical inspection station that recorded this inspection image. Inanother example, the user may define a bounding box around a threadedfastener in an inspection image of a representative assembly unit at aparticular assembly stage and link this bounding box to a data streamfor torque, dwell, and rotation count values output by a screw driver atan assembly station on the assembly line immediately preceding anoptical inspection station that recorded this inspection image. Thecomputer system can interface with the user to repeat this process foreach other manufacturing step, data stream, or non-visualmanufacturing-related data source imported into the new inspectionprocess in order to link these steps, data streams, and data sources toparticular features, components, or regions depicted in representativeinspection images of assembly units of this assembly type at particularstages of assembly.

The computer system can similarly interface with the user to linkcomponent supplier data, component characteristics, and/or othercomponent-related data to particular features, components, or regionsdepicted in representative inspection images of assembly units of thisassembly type at particular stages of assembly.

The computer system can extract spatial links between these non-visualmanufacturing data streams and features, components, or regions depictedin representative inspection images of assembly units of this assemblytype at particular stages of assembly thus tagged or annotated by theuser within the user portal. The computer system can then compile thesespatial links into a feature map defining spatial associations between:these non-visual manufacturing data streams; stages of assembly of theassembly type (e.g., defining relative time markers for production cycleof the assembly type); and (relative) physical locations of particularfeatures, components, and/or regions in this assembly type.

7.1 Temporal Segmentation

Furthermore, once the computer system has generated this feature mapdefining these spatial associations, the computer system can definetemporal links between segments of these data streams and particularfeatures, components, or regions of individual assembly units producedon the assembly line. In one example, the computer system: accesses acorpus of timestamped inspection image captured by a series of opticalinspection stations on the assembly line; implements methods andtechniques described in U.S. patent application Ser. No. 15/407,158 toidentify unique assembly units represented in these inspection images;and define groups of inspection images, each inspection image groupdepicting a single assembly unit at each imaged stage of assembly. Foreach inspection image group, the computer system can: sort inspectionimages in the inspection image group by timestamp; calculate a set ofassembly stage windows based on timestamps between each pair ofconsecutive inspection images in the group; and then tag each assemblystage window with an identifier of an assembly stage associated with aportion of the assembly line preceding the optical inspection stationthat recorded the second inspection image in the consecutive pair ofinspection images that define this assembly stage time window.Therefore, the computer system can automatically derive a time windowfor each assembly stage of an assembly unit based on timestamps ofinspection images of the assembly unit recorded by optical inspectionstations installed at known locations along the assembly line relativeto assembly stations at which these assembly stages are completed. Thecomputer system can additionally or alternatively define or refine theseassembly stage time windows for individual assembly units based ontimestamps of scan data—such as of barcodes applied to individualassembly units or to fixtures assigned to these assembly units—recordedas assembly units enter and/or exit assembly stations along the assemblyline.

The computer system can then segment non-visual manufacturing data intoclusters of data recorded by sensors, actuators, tools, robotic systems,etc. deployed within a particular assembly station or near a particularsection of the assembly line during a particular time window in which aparticular assembly unit was present in this particular assembly stationor near this particular section of the assembly line. The computersystem can repeat this process for each other assembly station and/orsection of the assembly line in order to aggregate specific clusters ofnon-visual manufacturing data that represent environmental conditions,tool conditions, machine actions, operator descriptors, etc.specifically encountered by the particular assembly unit. The computersystem can repeat this process for each other assembly unit imaged alongthe assembly line, thereby temporally segmenting these data streams bycorresponding assembly unit and manufacturing step.

However, the computer system can implement any other methods andtechniques: to generate a feature map linking manufacturing steps anddata output by sensors and actuators along the assembly line to discretecomponents, areas, or volumes within assembly units of this type; and tosegment these sensor and actuator data by temporal relationship toindividual assembly unit (or small groups of assembly units) producedalong the assembly line over time.

8. First Defective Assembly Unit and Defect Origin Hypothesis

As shown in FIGS. 4, 5A, and 5B, once a defect is detected in anassembly unit during production on the assembly line, the user may entercharacteristics of this defect into the computer system and submit ahypothesis for a spatial or temporal origin of this defect—that is, alocation on the assembly unit or a manufacturing input into the assemblyunit that the user predicts yielded this defect.

For example, upon completion of assembly or of an assembly step, anassembly unit maybe inspected manually for aesthetic defects and/or itsoperation tested for functional defects. When such a defect is thusidentified, the user may: access the user portal; enter a serial numberof the assembly unit; select a defect type from a dropdown menuprepopulated with known defect types or define a new defect type; enteradditional parameters of the defect (e.g., magnitude of a defectivefunction, dimension of an aesthetic defect); and then link the defecttime and related parameters to the assembly unit serial number. Inanother example, the user may view inspection images of assembly unitsproduced on the assembly line via the user portal and then selectivelywrite a defect tag or label to an inspection image of a defectiveassembly unit. In these examples, the computer system can thus storedefect types and related parameters for a defect present in the assemblyunit.

The computer system can then interface with the user to define ahypothesis for a spatial and/or temporal origin for the defect. Thecomputer system can also record definitions and hypotheses for multipleinstances of the same defect present across multiple assembly unitsand/or for multiple different defects present across multiple assemblyunits produced on this assembly line.

8.1 Point-Based Defect Hypothesis

The computer system can then prompt the user to supply a hypothesis fora special or temporal origin of this defect, such as: a feature,component, or region on the assembly unit at a part assembly stage(hereinafter a “region of interest”) that the user anticipates may havecaused on contributed to the defect; or a time window or manufacturingstep that the user anticipates resulted in a change on an assembly unitthat yielded this defect. For example, the computer system can access asequence of inspection images of the assembly unit and present theseinspection images to the user through the user portal. The user may thenselect one of these inspection images and select a singular pixel—in theinspection image—depicting an edge, component, subassembly, or otherregion of the assembly unit corresponding to an hypothesized origin ofthe defect. In this example, the computer system can implement methodsand techniques described below: to weight visual features extracted fromthe inspection image proportional to spatial proximity to this pixel;and to weight temporal features defined in the feature map proportionalto temporal proximity to a change in a narrow region of the assemblyunit depicted by this pixel and/or proportional to temporal proximity toa manufacturing step effecting this narrow region of the assembly unit.The computer system can then aggregate these visual and non-visualfeatures—thus associated with this region of interest and biasedaccording to these weights—into a vector or other container.

8.2 Area-Based Defect Hypothesis

Alternatively, the user may draw a bounding box over a region ofinterest on this inspection image to indicate a hypothesized location inthe assembly unit containing features or components predicted by theuser to have yielded the defect. In this example, the computer systemcan implement methods and techniques described above and below: toextract visual features contained in the region of interest of theinspection image; and to retrieve non-visual features linked to visualfeatures, components, subassemblies, manufacturing steps, or othermanufacturing inputs occurring inside of or otherwise effecting thisregion of interest in the assembly unit, as defined by the feature mapand segmented according to time windows of manufacturing steps for theassembly unit. The computer system can then aggregate these visual andnon-visual features thus associated with this region of interest into avector or other container.

8.4 Manufacturing Input Hypothesis for Defect

In another implementation, the user may submit a hypothesis for a linkbetween the test-result-based defect and a particular manufacturinginput—rather than for a link between the defect and a particular regionof interest on the assembly unit. For example, the user may select: aparticular manufacturing step that she anticipates yielded the defect,such as by selecting this particular manufacturing step from a list ofmanufacturing steps codified for the assembly type; an assembly stationat which she anticipates the defect occurred; or a period or assemblystage in which she anticipates the defect occurred.

Accordingly, the computer system can leverage the feature map to:isolate a particular time or time window in which the assembly unit wasmodified by or exposed to the particular manufacturing input; select aninspection image of the assembly unit recorded nearest (and after) thisparticular time or time window; and isolate a region of interest withinthis inspection image that differs from an immediately-preceding imageof the assembly unit (and that is therefore likely to represent a changein the assembly unit occurring due to the particular manufacturinginput).

The computer system can therefore interface with the user to link a newdefect to either a region of interest in an inspection image of anassembly unit or to a manufacturing input, and the computer system canimplement the feature map to isolate corresponding non-visualmanufacturing data or to define a region of interest, respectively, forthis assembly unit.

8.5 Defect Report

Yet alternatively, when a new defect is identified in an assembly unit,the user may upload a defect report to the computer system via the userportal, and the computer system can extract defect-related data fromthis report—such as defect type (e.g., “antenna failure,” “batteryfailure”), magnitude (e.g., “42% reduction in antenna performance”), andone or more locations or manufacturing steps hypothesized to have causedthis defect—and write these defect-related data to a file or othercontainer associated with the assembly unit.

8.6 Defect Identification by Other Stakeholders

Additionally or alternatively, a technician on the assembly line maymanually identify a defect in an assembly unit during its assembly andthen manually indicate presence and/or location of this defect through atechnician portal. The technician portal can then generate and link adefect flag to a digital file or other database corresponding to thisassembly unit. S1milarly, an engineer of inspector performing inspectionof an assembly unit during or upon completion of the assembly unit—suchas at a quality control station—may similarly identify a defect andwrite a defect flag to a digital file or other database corresponding tothis assembly unit. The computer system can then retrieve defect datafor assembly units produced on the assembly line from these digitalfiles or database.

8.7 Test-Based Defect Identification

In another implementation, rather than label a region of interest on anassembly unit as defective, the user may label a test result value orrange for a test designated for this assembly type as defective. Thecomputer system can also interface with the user: to link this test to aparticular manufacturing input that the user predicts might yield thisdefect in this assembly type (e.g., by selecting from a list ofmanufacturing steps codified for the assembly type); or to link thistest result to a region of interest in which the user predicts a featurepresent in this assembly type might cause this defect (e.g., by manuallyselecting a pixel or defining a bounding box around the region ofinterest in a representative inspection image of this assembly type).

The computer system can automatically scan test results for past and/orfuture assembly units for a test result that matches the test resultvalue or falls within the test result range thus defined for this defectand then flag individual assembly units as defective accordingly,including writing a stored spatial or temporal hypothesis for the originof this defect to each of these flagged assembly units.

8.8 Defect Origin Hypothesis by Assembly Stage

In one implementation, the computer system can interface with the userto define a spatial hypothesis for an origin of the defect in the formof one region of interest on the assembly type at one target stage ofassembly. Thus, in this implementation, the computer system can promptthe user to indicate a target assembly stage hypothesized by the user tocontain the origin of the defect in the assembly unit and then annotatean inspection image of the assembly unit—at the target assemblystage—with a region of interest hypothesized by the user to contain theorigin of this defect.

Alternatively, the user may elect to dissociate the region of interestfrom a particular assembly stage and instead define this region ofinterest for the defect hypothesis across multiple or all assemblystages for the assembly type, such as if multiple manufacturingprocesses are applied within the region of interest throughout variousassembly stages of the assembly type.

For example, the computer system can interface with the user to define aregion of interest over a first inspection image of the assembly unit ina first assembly stage. The computer system can then: prompt the user toconfirm whether the region of interest applies to a single targetassembly stage, to a subset of assembly stages, or to all assemblystages for the assembly type; and aggregate a set of inspection imagesof the assembly unit according to each assembly stage thus selected bythe user. For a second assembly stage thus selected by the user, thecomputer system can: retrieve a second inspection image of the assemblyunit; implement methods and techniques described in U.S. patentapplication Ser. No. 15/407,158 to align the second inspection image tothe first inspection image by aligning a second constellation offeatures detected in the second inspection image to an analogous firstconstellation of features contained in the region of interest in thefirst inspection image; and project a bounding box of the region ofinterest onto the second inspection image based on the secondconstellation of features and a relative position of the region ofinterest to the first constellation of features in the first inspectionimage. The computer system can repeat this process for each assemblystage selected by the user to define multiple cospatial regions ofinterest across multiple assembly stages of the assembly unit.

The computer system can extract visual (e.g., spatial) features fromregions of interest in each of these inspection images at differentassembly stages of the assembly unit. The computer system can alsoaggregate a set of manufacturing inputs effecting this region ofinterest across these assembly stages, isolate manufacturing input datastreams related to this set of manufacturing inputs, define time windowsof interest for these manufacturing input data streams and the assemblyunit, and retrieve manufacturing input data (i.e., non-visual features)recorded within these time windows based on the feature map. Thecomputer system can then assemble these visual and non-visual featuresinto a vector or other container representative of the assembly unit.

For example, the computer system can: retrieve each other inspectionimage depicting the assembly unit at other stages of assembly; align thedefect-annotated inspection image and this set of other inspectionimages of the assembly unit by a global feature present in each of theseinspection images or by a reference feature near the selected region ofinterest (or pixel) in some or all of these inspection images, such asdescribed in U.S. patent application Ser. No. 15/407,162; and thenproject the region of interest (or pixel) onto each other inspectionimage. The computer system can then compare this region of interestacross consecutive inspection images of the assembly unit to detectchanges occurring in this region of interest throughout assembly of theassembly unit. The computer system can then leverage the feature map toidentify a subset of manufacturing inputs occurring spatially andtemporally proximal these detected changes in the region of interest inthe assembly unit during its assembly and incorporate these non-visualfeatures into the vector or container for the assembly unit.

The computer system can also: extract visual features from regions ofthese inspection images depicting the region of interest of the assemblyunit in different assembly stages; and incorporate these visual featuresinto the vector or container for the assembly unit.

8.9 Defect Origin Hypothesis by Temporal Feature

In another implementation shown in FIG. 5B, the computer systeminterfaces with the user to define a non-visual or temporal hypothesisfor an origin of the defect, such as in the form of selection of: astage of assembly; a manufacturing step; an environmental condition; anassembly station; a station operation; or a component batch identifier;etc. The computer system can then: query the feature map to identifyregions of interest in the assembly type containing features that areadded to the assembly type, modified within the assembly type, orotherwise exposed to this non-visual or temporal hypothesis. Thecomputer system can therefore automatically define a region of interestcontaining visual features on the assembly type based on a non-visual ortemporal hypothesis for the origin of the defect and then implement theforegoing methods and techniques to aggregate visual and non-visualfeatures proximal this non-visual or temporal hypothesis based on thefeature map. In this implementation, the computer system can alsoautomatically reduce a size of the region of interest on the assemblytype—thus derived from the non-visual or temporal hypothesis for anorigin of the defect based on the feature map—in order to decrease aquantity of visual features extracted from the region of interest ifthis quantity exceeds a target or threshold quantity allocated forvisual features for this defect origin search; and vice versa, as shownin FIG. 5B.

8.10 Automatic Defect Origin Hypothesis

In one variation, the computer system automatically generates ahypothesis for the origin of the defect based on a type of the defectand assembly type functions linked to specific regions of the assemblytype in the feature map. For example, in response to receipt of a defectspecifying an antenna functional test failure in an assembly unit, thecomputer system can: query the feature map for a region in one or moreupstream inspection images linked to antenna function; and then storethis region as a spatial hypothesis for the origin of the defect. Inthis example, the computer system can additionally or alternatively:query the feature map for an assembly step, station or process in whichcomponents or subassemblies affiliated with antenna function—accordingto the feature map—are installed on assembly type or otherwise modifiedon the assembly type; and then store this assembly step, station orprocess as a temporal hypothesis for the origin of the defect.

9. Feature Aggregation

Once a defect is defined for the assembly type and once the computersystem identifies a set of (e.g., at least a threshold number of)assembly units exhibiting this defect, the computer system canautomatically: aggregate a set of visual features and non-visualmanufacturing data exhibiting high temporal and spatial proximity tooccurrence of the defect in a particular assembly unit based on thefeature map; repeat this process for other assembly units exhibiting andnot exhibiting the defect; and then implement artificial intelligence,machine learning, regression, statistical analysis, and/or other methodsand techniques to calculate correlations between these visual andnon-visual features and the defect across this population of assemblyunits.

In one implementation, a particular defect is associated with aparticular region of interest for the assembly type. For an assemblyunit produced on the assembly line, the computer system can: retrieve aninspection image recorded immediately after a particular assembly stagein which features in the particular region of interest changed; projectthe region of interest associated with this defect onto the inspectionimage; crop the inspection image around the region of interest; detectand extract a set of visual features from this region of interest (orfilter an existing set of visual features already extracted from theseinspection images to include features within this region of interestonly); and populate a vector or other container with values representingthese extracted visual features, thereby generating a containerrepresenting a set of visual features exhibiting spatial proximity to apredicted source of a known defect for this assembly type.

In this implementation, the computer system also can: query the featuremap of this assembly type for manufacturing inputs (i.e., non-visualfeatures) associated with this region of interest and falling within anassembly time window associated with this defect (e.g., within a timewindow between the particular assembly stage and a preceding assemblystage); aggregate a subset of non-visual manufacturing datacorresponding to these manufacturing inputs for this assembly unit; andappend these non-visual manufacturing data to the vector representingthis assembly unit. In particular, the computer system can leverage thefeature map to isolate non-visual manufacturing data recorded duringproduction of this assembly unit between the particular assembly stageand the preceding assembly stage and affecting features within theregion of interest and to inject these non-visual manufacturing datainto the vector associated with this assembly unit in order to generatea container including both visual and non-visual features exhibitinghigh spatial and temporal proximity to a predicted source of theparticular defect. For example, the computer system can write: ambienttemperature and humidity values within the manufacturing facility ornear the assembly line during the assembly time window for this assemblyunit; an ID of a technician—working at an assembly station in which theregion of interest for the assembly type is modified—during the assemblytime window for this assembly unit; a torque reading of a screwdriverused at this assembly station during this assembly time window; a batchID of a component designated for the region of interest and available atthe assembly station during this assembly time window; etc. to thevector or other container for this assembly unit.

9.1 Feature Filter

In one variation shown in FIGS. 5A and 5B, if a defect hypothesissubmitted by the user yields more than a maximum number of visual andnon-visual features, the computer system can filter these features inorder to limit the size of the resulting vector.

In one implementation, if the defect hypothesis defines a region ofinterest on an inspection image of a defective assembly unit, thecomputer system can: extract visual features from this region ofinterest of the assembly unit at a particular assembly stage depicted inan inspection image; query the feature map for non-visual features(e.g., manufacturing inputs) that effect this region of interest, suchas within, before, and/or after this assembly stage; collate timeseriesmanufacturing input data for these non-visual features and for thisassembly unit; and aggregate this set of visual and non-visual features.

If the quantity or size of this set of visual and non-visual featuresexceeds a threshold size (e.g., 4,000 features), the computer system canimplement methods and techniques described below to rank these visualfeatures, such as: by proximity to the center of the region of interest;by proximity to a largest visual feature in the region of interest; orby proximity to a target point, pixel, or feature in the region ofinterest; etc. The computer system can similarly rank these non-visualfeatures, such as: by proximity of corresponding manufacturing processesto the center of the region of interest; by proximity to a largestvisual feature in the region of interest; by proximity to a time theinspection image of the assembly unit was recorded; and/or by proximityto the assembly stage of the assembly unit depicted in the assemblyunit; etc. The computer system can then: discard the lowest-rankedvisual and non-visual features in this set in order to assemble afeature set of the threshold quantity or size; store this filtered setof visual and non-visual features in a vector (or other container) forthe defective assembly unit; and label the vector with an identifier ofthe defect.

In another implementation, if the defect hypothesis defines a region ofinterest on an inspection image of a defective assembly unit, thecomputer system can: allocate a fixed quantity of visual features (e.g.,3,000 visual features and 1,000 non-visual features) or a dynamicquantity inversely proportional to a quantity or density of non-visualfeatures available for this region of interest; and then extract visualfeatures from the region of interest in the inspection image at afeature density that produces this allocation of visual features.S1milarly, the computer system can extract a set of visual features fromthe region of interest of the inspection image at a feature densityinversely proportional to a size (e.g., an area) of the region ofinterest.

In the foregoing implementation, the computer system can also set thetarget quantity of visual features extracted from an inspection imageproportional to a total quantity of assembly units produced and imagedon the assembly line. More specifically, the computer system can extracta greater quantity of visual features (or “dimensions”) from a region ofinterest in an inspection image of an assembly unit if a largerpopulation of images of more assembly units at this same assembly stageare available, thereby enabling the computer system to “look deeper”into these assembly units for smaller features that may have caused thedefect while also leveraging a larger population of inspection images toreject spurious correlations (or “noise”) between features in thishigher-dimension feature set and the defect.

9.2 Feature Aggregation for Other Assembly Units

As shown in FIGS. 5A and 5B, the computer system can repeat theforegoing processes to generate similar vectors for many other assemblyunits produced on the assembly line. For example, the computer systemcan generate a vector definition (e.g., a set of physical locations,relative time windows, and data sources) for visual and non-visualfeatures represented in a first vector of a defective assembly unitlabeled by the user. The computer system can then repeat the foregoingprocesses to generate like vectors for (many, all) other assembly unitscompleted on the assembly line according to this vector definition. Thecomputer system can also label each of these vectors with an outcome ofthe corresponding assembly unit. For example, for each assembly unit inwhich the defect was confirmed (e.g., by the user, by another system, astation operator), the computer system can label the correspondingvector with the identifier of the defect.

In one variation, when a known defect is detected in a subsequentassembly unit, the user may manually tag a file or other containerassociated with this new assembly unit with the defect. Additionally oralternatively, as reports (e.g., test results) for subsequent assemblyunits produced on the assembly line are published to a database, thecomputer system can automatically: scan these reports for test resultsor other values associated with the known defect; and identify selectassembly units as defective accordingly. The computer system can also:automatically scan inspection images of subsequent assembly units forfeatures associated with the known defect; identify select assemblyunits as defective accordingly, such as described in U.S. patentapplication Ser. No. 15/953,206; and prompt the user to confirm presenceof the defect in these other assembly units.

The computer system can additionally or alternatively prompt the user tomanually identify other assembly units of the assembly type in which thedefect was detected. However, the computer system can implement anyother method or technique to automatically identify defective assemblyunits, to access identities of assembly units manually identified asdefective by the user or other operator, or to access identities ofassembly units identified as defective by other systems.

9.3 General Defect Origin Hypothesis

In one variation, the computer system interfaces with the user to recordpresence of a defect in a batch of assembly units of an assembly typeand to define a spatial or temporal hypothesis for the origin of thisdefect in this assembly type generally rather than for a particularassembly unit (e.g., by defining an region of interest over a CAD modelor other virtual representation of the assembly type or flagging amanufacturing step in a set of manufacturing steps extracted frommanufacturing documentation). The computer system then: generates avector definition for the assembly type generally accordingly; projectsthe region of interest onto inspection images of assembly units of thisassembly types; extracts features from this region of interest in eachof these inspection images according to the vector definition;aggregates non-visual features for each of these assembly unitsaccording to the vector definition; compiles these visual and non-visualfeatures into vectors uniquely representing each assembly unit; and thenlabels each of these vectors with the outcome of (i.e., presence of thedefect in) their corresponding assembly units.

10. Defect Correlation

The computer system can then implement artificial intelligence, machinelearning, regression, statistical analysis, and/or other methods andtechniques—such as described in U.S. patent application Ser. No.15/953,206—to compare these vectors and to isolate a singular feature ora cluster of features that are common to vectors associated withassembly units exhibiting the particular defect but generally absentfrom vectors associated with assembly units not exhibiting theparticular defect (or vice versa). In particular, the computer systemcan execute Blocks of the method S100 to: generate “short” vectorsrepresenting a relatively small number of visual and non-visual featurestemporally and spatially proximal (or contained within) a hypothesizedorigin of a defect for a population of assembly units; and then comparethese vectors in order to isolate a subset of visual and/or non-visualfeatures exhibiting strong covariance with presence of this defect inthese assembly units.

10.2 Weighted Features

In one implementation, for each (of many) assembly units produced on theassembly line, the computer system compiles visual features extractedfrom inspection images of the assembly unit and non-visual manufacturingdata collected during production of the assembly unit into an unweightedvector or other container. When a defect is detected in an assembly unitand confirmed by the user, the computer system can calculate a weightfor each visual and non-visual feature represented in these vectorsbased on temporal and spatial proximity to the defect, as defined in thefeature map. In particular, the computer system can devalue or mutevisual and non-visual features substantially remote from a location ofthe defect, from a predicted location of a source of the defect, and/orfrom a time window (e.g., between two assembly stations) in which thedefect presented in assembly units of this assembly type. Conversely,the computer system can emphasize visual and non-visual featuressubstantially proximal the location of the defect, from the predictedlocation of a source of the defect, and/or from the time window in whichthe defect presented in assembly units produced on the assembly line.

The computer system can then process vectors—including values thusweighted by temporal and spatial proximity to the defect andrepresenting a population of assembly units—according to artificialintelligence, machine learning, regression, statistical analysis, and/orother methods and techniques in order to derive correlations betweenthese features and this defect. By thus weighting these featuresaccording to the feature map, the computer system may reduce a time forthe computer system to generate these correlations and increase accuracyof these correlations.

10.3 Feature Mask

In a similar implementation, the computer system can implement thefeature map to: identify a particular subset of features—represented invectors corresponding to a population of assembly units—that aretemporally and spatially proximal a region of interest associated with adefect in this assembly type; to mask (e.g., weight to “0” or otherwisedeemphasize) all other features in these vectors; and then implementartificial intelligence, machine learning, regression, statisticalanalysis, and/or other methods and techniques to calculate degrees ofcovariance between features in the particular subset of features and thedefect. By thus implementing the feature map as a mask to isolatefeatures that are spatially and temporally close to (e.g., with a timeand distance threshold of) the defect and therefore more likely to havecaused the defect, the computer system can reduce processing load,increase processing speed, and increase accuracy of a derive correlationbetween a particular features and the defect.

In yet another implementation, for each assembly unit produced on theassembly line, the computer system can generate a vector representingboth visual features detected in a set of inspection images andnon-visual manufacturing data recorded during assembly of the assemblyunit. The computer system can implement artificial intelligence, machinelearning, regression, statistical analysis, and/or other methods andtechniques to generate a covariance matrix representing correlationsbetween each of these visual and non-visual features and the defect. Thecomputer system can then implement the feature map to generate amask—for the covariance matrix—that mutes features that are more than athreshold distance from the defect in time and/or space or that devaluesthese features that are more than a threshold distance from the defectin time and/or space or that devalues these features proportional tospatial or temporal distance from the defect. By then applying this maskto the covariance matrix, the computer system can filter a (potentially)large set of features that exhibit covariance with presence of thedefect in this assembly type down to a relatively small subset offeatures likely to include a particular feature that caused or predictsthe defect.

However, the computer system can implement the feature map in any otherway to filter or weight visual and non-visual features based on temporaland spatial proximity to a known defect and can implement any othermethods or techniques to derive correlations between these features andthe defect.

11. Guidance

As shown in FIGS. 2-4, once the computer system derives a correlationbetween the defect and either a visual feature present in images ofassembly units and/or a non-visual feature representing a manufacturinginput, the computer system can: prompt investigation into the feature,such as if correlation between this feature and the defect exceeds athreshold score; and/or generate and serve guidance for reducingfrequency of this feature, which may reduce frequency of the defect.

In one implementation, if the computer system derives a strongcorrelation (e.g., high covariance) between the defect and a particularmanufacturing input, the computer system can transform this particularmanufacturing input directly into a manufacturing step or process toinvestigate for a root cause of the defect. For example, if the computersystem derives a strong correlation between the defect and a particularsensor stream output by a sensor at a particular assembly stage alongthe assembly line input, the computer system can: query the feature mapto identify a manufacturing step associated with or occurring near thissensor; and then generate guidance to investigate this manufacturingstep for a root cause of the defect.

However, if the computer system derives a strong correlation (e.g., highcovariance) between the defect and a particular visual feature, clusterof visual features, or region of inspection images recorded at aparticular stage of assembly (e.g., by a particular optical inspectionstation), the computer system can: reference the feature map to identifya particular manufacturing step or process in which a component ordetail of the assembly type—represented by this feature, cluster offeatures, or region of inspection images of these assembly units—iscreated, incorporated into, or changed in the assembly type; generate aprompt to investigate this particular manufacturing step or process fora root cause of the defect; and then return this prompt to the user. Forexample, the computer system can derive and update a correlation betweenthe defect and a particular cluster of features depicted in inspectionimages of assembly units at a particular assembly stage as theseinspection images are recorded by an optical inspection station at thisparticular assembly stage or as these inspection images are madeavailable to the computer system. Once this correlation exceeds athreshold score (or if this correlation exceeds correlations between thedefect and all other tested features of the assembly type), the computersystem can: query the feature map to identify a component or subassemblythat spatially intersects this particular cluster of features; query thefeature map to identify a manufacturing step in which this component orsubassembly is installed or modified within assembly units of thisassembly type; and then generate guidance to investigate thismanufacturing step for a root cause (or “origin”) of the defectaccordingly.

The computer system can implement similar methods and techniques toderive and handle correlations between a defect and a characteristic ofa supplied component and/or combinations of visual and/or non-visualfeatures of assembly units of this assembly type.

The computer system can then serve this guidance to the user in order toprompt investigation of a particular manufacturing step, environmentalcondition, or component characteristics, etc. for causation of thedefect. For example, the user may modify a manufacturing step, amanufacturing tolerance, tooling, tools, jigs, fixtures, and/or anassembly technician, etc. responsive to this guidance in order to reducefrequency, magnitude, or range of this particular feature, which mayreduce frequency or magnitude of this defect in subsequent assemblyunits produced on the assembly line.

Therefore, the computer system can generate and serve guidance to theuser to prompt the user to investigate a particular control input—suchas an equipment setting or condition of a fixture—that exhibits highcovariance with a defect in a population of assembly units.(Alternatively, the computer system can automatically modify thisparticular control input, such as by interfacing directly with equipmentto modify this setting, as described below.)

The computer system can also: isolate a dimensional measurement or testresult value or range that exhibits high correlation to a particulardefect; generate a notification indicating this correlation and a promptto investigate this dimensional measurement or test; and serve thisnotification to the user (or to a technician) in order to triggerinvestigation into this particular dimensional measurement or testresult and its relationship with the particular defect. S1milarly, thecomputer system can serve guidance to the user or to a technician on theassembly line to inspect or discard assembly units that exhibit thisdimensional measurement or test result.

Therefore, the computer system can execute Blocks of the method S100 toisolate a non-visual manufacturing input (e.g., a control input, adimensional value, a test result) that exhibits a high degree ofcovariance with a known defect and to notify the user or a technician ofthis correlation in order to prompt: an investigation into thismanufacturing input at the assembly line; a change to this manufacturinginput at the assembly line; or automatic discard or rework of assemblyunits that exhibit this manufacturing input.

11.1 Guidance Interface

In one variation, the computer system returns a list of visual andnon-visual features—ranked by strength of correlation to the defectand/or exceeding a threshold correlation score for the defect—to theuser portal in order to inform selective, targeted investigation intothese features as possible root causes of the defect. For example, thecomputer system can: generate a list of assembly steps, a list ofmanufacturing processes, a list of assembly stations and/or stationoperators, a list of daily time windows, a list of environment conditionranges, and/or a list of coordinate positions of physical (i.e., visual)features that occur concurrently with presence of the defect in assemblyunits with high frequency; sort objects in these lists by strength ofcorrelation to the defect; and return these lists—such as in the form oftextual descriptions with quantitative or qualitative values and/or ascoordinate values—to the user. The computer system can also serve aprompt to the user to investigate features in these lists for the originof the defect, such as in the order presented on these lists.

The computer system can additionally or alternatively overlay arepresentative inspection image of the assembly type with a heatmapindicating strengths of correlations of features depicted in theinspection image to the defect. For example, the computer system can:retrieve a representative inspection image of an assembly unit of theassembly type; generate a heatmap depicting strengths of correlations ofvisual features—present in the region of interest for this defecthypothesis for the assembly type—to the defect; and render the heatmapover the representative inspection image. In this implementation, thecomputer system can also: populate positions over this representativeinspection image—that correspond to non-visual manufacturing featuresexhibiting more than a threshold correlation to the defect—with a set offlags; and link each of these flags to a non-visual manufacturingfeature exposed to, effecting, or otherwise associated with the assemblytype at the location of the flag according to the feature map. Thecomputer system can then: serve this annotated inspection image to theuser portal for review by the user; and retrieve non-visualmanufacturing input data (e.g., live data streams, or raw timeseriesdata for a defect assembly unit) when a flag in this annotatedinspection image is selected at the user portal.

11.2 Example

In one implementation, the computer system implements methods andtechniques described above to: access a set of production documents forthe assembly type; interpret a sequence of manufacturing steps for theassembly type represented in the set of production documents; andinterface with the user to define and record a first set of linksbetween each manufacturing step—in this set of manufacturing steps—and asubregion of the particular assembly type at one or more assembly stages(e.g., by annotating a CAD model of the assembly type or annotatingrepresentative inspection images of an assembly unit of the assemblytype at various stages of assembly with links to these manufacturingsteps). In this implementation, the computer system can interface withthe user to: record a bounding box—drawn manually by the user over a CADmodel or an inspection image of a representative assembly unit of theassembly type—around a component added to the particular assembly typeduring a manufacturing step; define a subregion of the assembly effectedduring this manufacturing step according to the bounding box; write apointer for the manufacturing step to this assembly subregion; andrepeat this process for each other manufacturing step thus associatedwith this assembly type. In this implementation, the computer systemcan: similarly interface with the user to define and record a second setof links between a set of manufacturing input data streams andmanufacturing steps in the set of manufacturing steps; and then compilethe first and second sets of links into a feature map for the assemblytype.

For example, for a particular manufacturing step in this set, thecomputer system can: record selection of a threaded fastener—depicted ina particular subregion in an inspection image of a representativeassembly unit of the assembly type at a particular stage ofassembly—installed on the particular assembly type during the particularmanufacturing step; and then link a particular subregion of theparticular assembly type containing the threaded fastener to thisparticular manufacturing step. In this example, the computer system canalso: receive selection of a particular data stream—containing torqueoutput values and torque dwell values output by a screwdriver—enteredvia the user portal; and then link this particular data stream to theparticular manufacturing step. Accordingly, the computer system can linkthe particular data stream to the particular subregion of the particularassembly type and to the particular stage of assembly (and therefore toa time window of the particular stage of assembly for an assembly unitof this assembly type). Later, the computer system can calculate:correlations between torque output values of the screwdriver, torquedwell values of the screwdriver, and a defect for assembly units of thisassembly type; and prompt the user to investigate screwdriver torqueand/or screwdriver dwell for this manufacturing step if correspondingcorrelations to the defect exceed a threshold correlation and/or exceedcorrelations of other visual and non-visual features near this region ofinterest and near the particular assembly stage.

11.3 Automatic Defect Detection

In one variation shown in FIG. 5A, the computer system can implement anyother methods or techniques to scan inspection images and/or non-visualmanufacturing input data for additional assembly units that exhibitvisual or non-visual features correlated with the defect and to thenprompt the user to inspect these other assembly units for the defect.

For example, once the user confirms a spatial or temporal feature as aroot cause of a defect, the computer system can: search inspectionimages and manufacturing input data for a subset of other assembly unitsproduced on the assembly line for the same or similar spatial ortemporal feature; and automatically flag this subset of other assemblyunits for investigation for presence of the defect, such as bygenerating a list of serial numbers of these flagged assembly units andserving this list of serial numbers to the user to inform selective,targeted investigation of assembly units produced on this assembly linefor presence of this defect.

12. Failed Correlation Search

In one variation shown in FIGS. 5A and 5B, if the computer systemexecutes the foregoing processes based on a spatial or temporalhypothesis for the defect but fails to identify a visual or non-visualfeature that exhibits a correlation to the defect greater than athreshold correlation, the computer system can prompt the user to enteran alternate hypothesis for the defect and then repeat the foregoingprocesses to retest this alternate hypothesis for visual or non-visualfeatures that exhibit stronger correlation to the defect.

For example, if magnitudes of correlations between visual features(extracted from the region of interest within inspection images ofassembly units or proximal a more specific target location in theseinspection images), subsets of non-visual manufacturing features(associated with locations on these inspection images within the regionof interest or proximal the target location), and the defect remainbelow a threshold correlation (e.g., 0.70), the computer system canprompt the user to define an alternate region of interest (or analternate target location) hypothesized by the user to contain analternate origin of the defect for this assembly type. Then, for eachassembly unit in this population of assembly units manufactured on theassembly line, the computer system can: project this alternate targetlocation onto the inspection image of the assembly unit; detect a secondset of visual features proximal the second target location (or within asurround region of interest) in the inspection image; and aggregate asecond set of non-visual manufacturing features associated withlocations proximal the second target location (or within the region ofinterest) for this assembly unit. The computer system can then calculatea second set of correlations between this second set of visual andnon-visual manufacturing features across the population of assemblyunits and the defect occurring within the population of assembly units,as described above. The computer system can repeat this process toingest new hypotheses for the origin of the defect for the assembly typeuntil the computer system identifies a visual or non-visual feature withcorrelation to the defect that exceeds the threshold correlation.

Alternately, the computer system can record multiple spatial and/ortemporal hypotheses for the origin of the defect for the assembly typeand then sequentially execute Blocks of the method S100 for each ofthese hypotheses until a correlation between a feature in one of thesehypotheses and the defect exceeds the threshold correlation.

13. Multiple Concurrent Defect Origin Hypotheses

In one variation, the computer system interfaces with the user to definemultiple spatial and/or temporal hypotheses for the origin of a defectin an assembly unit. For example, the computer system can interface withthe user according to the forgoing methods and techniques to define aquantity of hypotheses for the origin of the defect: inverselyproportional to an area (e.g., length and width) of a region of interestdefined on an inspection image of the defect assembly unit; inverselyproportional to a quantity of features exceeding a threshold size orweight present in a region of interest defined on the inspection imageof the defect assembly unit; inversely proportional to a number ofassembly stages affiliated with the region of interest; inverselyproportional to a duration of the assembly stage(s) affiliated with theregion of interest; and/or inversely proportional to a quantity ofmanufacturing input data streams affiliated with the region of interestor these the assembly stage(s). for each region of interest thus fardefined for the assembly unit. In particular, the computer system canenable the user to input or define a quantity of additional hypothesesfor the origin of the defect in the assembly unit inversely proportionalto a quantity of visual and non-visual features available for defecthypotheses already submitted for the assembly unit.

For example, when presence of a defect in an assembly unit is indicatedto the computer system, the computer system can record a boundingbox—drawn manually over a subregion of a representative inspection imageof the assembly unit via the user portal—thus hypothesized by the userto contain the origin of the defect, as described above. If an area ofthe bounding box falls below a threshold area, if a quantity of assemblyunits of this assembly type exceeds an absolute threshold quantity,and/or if a ratio of the quantity of assembly units of this assemblytype to the area of the bounding box exceeds a threshold ratio, thecomputer system can: prompt the user to enter a second hypothesis forthe origin of the defect; record a second bounding box—drawn manuallyover a second subregion of an inspection image of the assembly unit inthe same or other assembly stage—hypothesized by the user to contain theorigin of the defect; and repeat the foregoing processes to extractfeatures from a second region of interest defined by this secondbounding box and to aggregate non-visual features associated with thissecond region of interest based on the feature map. The computer systemcan then compile these visual and non-visual features into a secondvector (or other container) for the assembly unit and the secondhypothesis.

The computer system can then execute Blocks of the method S100 to:generate similar pairs of vectors for the first and second hypothesesfor other assembly units of the assembly type; and derive correlationsbetween the defect and contents of the resulting set of first vectorsand the resulting set of second vectors for these assembly units. Thecomputer system can then implement deep learning, artificialintelligence, machine learning, regression, statistical analysis, and/orother methods and techniques to derive correlations between the defectand features represented in a population of vectors—representing asingle defect hypothesis—as described above and repeat this process foreach other population of vectors corresponding to one defect hypothesis.

Alternatively, the computer system can: compile visual and non-visualfeatures corresponding to both hypotheses for the defective assemblyunit into one vector; generate similar vectors for other assembly unitsof the assembly type; and derive correlations between the defect andcontents of the resulting set of vectors for these assembly units. Thecomputer system can then implement deep learning, artificialintelligence, machine learning, regression, statistical analysis, and/orother methods and techniques to derive correlations between the defectand features represented in a population of vectors—representingmultiple hypotheses for the defect—as described above.

However, the computer system can enable the user to enter additional(e.g., secondary, tertiary) hypotheses for the origin of the defect andcan selectively implement Blocks of the method S100 to test these defecthypotheses according to any other schema.

14. Closed-Loop

In one variation, after the user modifies a manufacturing input responseto guidance served by the computer system and while the assembly line isstill operational, the computer system can repeat the foregoing methodsand techniques to collect manufacturing data and inspection images ofthese new assembly units. The computer system can also compare these newmanufacturing data to historical manufacturing data to determine whethera change was made along the assembly line and then verify whether thischange resulted in a change in frequency or severity of the defect.

15. Variation: Automated Process Change

In one variation, rather than serve guidance to the user to modify aprocess along the assembly line to reduce frequency of the defect, thecomputer system can automatically modify a process (e.g., an equipmentsetting, an ambient condition, a supplier designated to supply aparticular component) based on a spatial and temporal location of adefect occurring in assembly units at a particular stage along theassembly line and based on a manufacturing input proximal thisparticular stage, as indicated in the feature map. The computer systemcan then repeat the foregoing methods to automatically check subsequentassembly units for this defect and can implement closed-loop controls tofurther modify this manufacturing process automatically based on degreesof this defect detected in these subsequent assembly units.

16. Variation: Preloaded Defect Predictions

In one variation, the computer system interfaces with the user togenerate a predefined set of possible defects for an assembly type andto link each possible defect to one or more defect modes, wherein eachdefect mode is linked to a region of interest and to an assembly timewindow (or discrete assembly time) for the assembly type. For example,the user may define a generic wireless antenna failure defect and definea set of defect modes including: “screw missing,” “crystal position tooclose to component,” “crystal position too far from component”, and“ambient humidity limit exceeded after reflow.” In this example, thecomputer system can interface with the user to link these defect modesto particular manufacturing steps, manufacturing data streams, and/oractuator actions along the assembly line.

When an assembly unit is later assembled, tested, and determined toexhibit a particular defect, the computer system can: access aninspection image recorded during the assembly time window specified fora defect mode of this defect; automatically project a stored region ofinterest for this defect mode on this inspection image; extract visualfeatures from this region of interest; aggregate manufacturinginputs—related to this region of interest and the assembly time windowby the feature map—for the assembly unit; compile these visual andnon-visual features into a vector or other container representing theassembly unit; and weight these features by physical and temporalproximity to the region of interest and to the assembly time window, asdescribed above.

The computer system can repeat this process to generate weighted vectorsrepresenting other assembly units of the same assembly type labeled asexhibiting this defect; and to generate weighted vectors representingother assembly units of the same assembly type labeled as not exhibitingthis defect. Accordingly, the computer system can then implement methodsand techniques described above to derive a correlation between aparticular physical feature and/or manufacturing input for the assemblyunit and this defect mode. The computer system can also: generatesimilar vectors for these assembly units based on regions of interestand assembly time windows specified in other predefined defect modes;and implement the foregoing methods and techniques to derivecorrelations between particular physical features and/or manufacturinginputs and each of these defect modes. Accordingly, the computer systemcan serve guidance to the user to investigate manufacturing steps alongthe assembly line for defects resulting from a related defect modeexhibiting greatest covariance to visual and non-visual featuresdetected in assembly units produced on the assembly line.

17. Variation: Outcome Range

In one variation, the computer system implements similar methods andtechniques to derive correlations between range of visual/non-visualassembly unit features and range of outcomes for the assembly type.

In one implementation, rather than ingest binary defect information(i.e., whether an assembly unit exhibits a defect), the computer systemcan ingest higher-resolution test data for assembly units produced onthe assembly line and then implement methods and techniques describedabove to isolate a small number of visual and/or non-visual assemblyunit features that exhibit high covariance with a range of test values.The computer system can then communicate a correlation between ranges ofthese features and these ranges of test values to the user, such as inthe form of a report or notification, as described above.

18. Variation: Anomaly Detection

In one variation, the computer system implements methods and techniquesdescribed in U.S. patent application Ser. No. 15/953,206: to comparevisual features within a population of completed or in-process assemblyunits; and to detect a visual anomaly in a particular region of anassembly unit at a particular stage of assembly. The computer systemthen queries the feature map for a set of manufacturing inputs relatedto this anomaly, such as ranked by: physical proximity of a locationassociated with the manufacturing input to the location of the defect inthe assembly unit; and by temporal proximity to the manufacturing inputto an manufacturing step occurring at or near the anomaly.

For example, the computer system can implement the feature map: toidentify a particular manufacturing step (e.g., insertion of a screw,application of an adhesive, placement of a part or subassembly)designated for a location within assembly units of this type at or nearthe location of the anomaly detected in this assembly unit; and toidentify a time during manufacturing of this assembly unit at which thisparticular manufacturing step occurred. The computer system can then:retrieve non-visual manufacturing data recorded along the assembly line,corresponding to the manufacturing facility more generally, related toparts supplied along the assembly line, etc.; link these non-visualmanufacturing data to discrete locations or regions within the assemblyunit based on the feature map; and calculate weights for thesenon-visual manufacturing data as an inverse function of physicaldistance to the location of the anomaly in the assembly unit andtemporal offset from the time of the particular manufacturing stepoccurred at the assembly unit. The computer system can then implementdeep learning, artificial intelligence, machine learning, regression,statistical analysis, and/or other methods and techniques—such asdescribed in U.S. patent application Ser. No. 15/953,206—to compareweighted manufacturing inputs between this anomalous assembly unit andother assembly units not exhibiting this anomaly in order to isolate asubset of manufacturing input exhibiting greatest correlation to thisanomaly (i.e., manufacturing inputs most likely to have led to theanomaly).

The computer system can then generate a report including: a prompt toinspect the assembly unit for this anomaly; manufacturing data for thesemanufacturing inputs exhibiting greatest correlation with the anomaly; aprompt to confirm whether the anomaly represents a defect; and a promptto confirm a link between the anomaly and these manufacturing inputs.The computer system can serve this notification to the user via the userportal (or to another engineer or technician at the assembly line)—suchas in real-time or following completion of the assembly unit. Thecomputer system can therefore: selectively prompt the user to provideguidance (i.e., “human supervision”) related to this anomaly; andintelligently suggest possible root causes of this anomaly (i.e., byranking or filtering manufacturing inputs by spatial and temporalproximity to the anomaly), thereby targeting feedback from the user,streamlining input of such feedback, and reducing a burden on the userto identify, verify, and investigate this anomaly.

In this implementation, the computer system can therefore: detect avisible anomaly in an inspection image of an assembly unit; leverage thefeature map to link this anomaly to other manufacturing inputs; weightthese manufacturing input by temporal and physical proximity to thisvisible anomaly; compare weighted non-visual manufacturing data betweenthe anomalous assembly unit and other assembly units not exhibiting thisanomaly in order to predict a manufacturing input that represents a rootcause of this anomaly; and to prepare a report for this anomaly,including both visual data depicting the anomaly and a suggestion for amanufacturing input related to this anomaly.

The computer system can implement similar methods and techniques: tocompare manufacturing inputs within a population of completed orin-process assembly units; and to detect a non-visual anomaly in one ora subset of these manufacturing inputs for a particular assembly unitduring assembly. The computer system can then query the feature map to:identify an assembly time or time window in which the assembly unit wasexposed to or modified according to this anomalous manufacturing input;select a particular inspection image of the assembly unit recordedduring or immediately following this assembly time or time window; andthen isolate a particular location or region of interest within thisparticular inspection image corresponding to a location on this assemblyunit modified by or otherwise exposed to this anomalous manufacturinginput.

The computer system can then implement artificial intelligence, machinelearning, regression, computer vision, statistical analysis, and/orother methods and techniques to: extract or aggregate features withinthis region of interest; project this region of interest onto likeinspection images of other assembly units excluding this anomaly innon-visual manufacturing data; extract or aggregate features within thisregion of interest in these other inspection images; weight thesefeatures by temporal and physical distance from the anomalousmanufacturing input; and compare features between this region ofinterest in these inspection images in order to isolate a subset ofvisual features exhibiting greatest correlation to this anomalousmanufacturing input (i.e., visual features that may depict a physicalresult of this anomalous manufacturing input). The computer system canthen: aggregate this anomalous manufacturing input and this region ofinterest or a more specific subset of features in the inspection imageof the assembly unit into a report; serve this report to the user viathe user portal; and thus prompt the user to provide selective feedbackto verify whether this anomalous manufacturing input has resulted in adefect in a location on the assembly unit depicted in this region ofinterest in the inspection image.

18.1 Examples

Therefore, in this variation, the computer system can detect ananomaly—rather than an explicit, predefined, or user-verified defect—innon-visual manufacturing data and/or in inspection images of assemblyunits produced on the assembly line, such as described in U.S. patentapplication Ser. No. 15/953,206.

In one example, the computer system identifies individual assembly unitsor cluster of assembly units that contain components in relativepositions, of sizes, and/or of geometries, etc. that differ fromconstellations of components in (many) other assembly units—of the sameassembly type—produced on the assembly line (e.g., labeled asfully-functional or non-defective). The computer system can then flagthese assembly units exhibiting this visual anomaly relative to thepopulation of completed assembly units of this assembly type.Accordingly, the computer system can then implement the foregoingmethods and techniques to: identify non-visual features proximal thisvisual anomaly based on the feature map; define time windows for thesenon-visual features specific to each assembly unit; aggregate thesenon-visual features into vectors or other containers for each assemblyunit; derive correlations between the anomalous visual feature and theseother non-visual features; isolate one or a subset of non-visualfeatures exhibiting greatest correlation to the anomalous visualfeature; and return a prompt to the user to inspect assembly units withthe anomalous visual feature in the context of the one or subset ofnon-visual features exhibiting greatest correlation to the anomalousvisual feature. Therefore, in this implementation, the computer systemcan automatically isolate particular manufacturing inputs that may havecontributed to a yet-uncharacterized physical anomaly in some assemblyunits and then present both the uncharacterized physical anomaly andthese particular manufacturing inputs to the user, thereby enabling theuser to quickly trace a root cause of the physical anomaly, determinewhether the physical anomaly yields a defective assembly unit, andaddress changes at the assembly line to directly reduce frequency ofthis physical anomaly.

In a similar example, the computer system can identify individualassembly units or clusters of assembly units for which screwdrivertorques during a particular assembly step, ambient humidity, heat staketool temperature, time between consecutive optical inspection stations,a time between consecutive assembly stages, and/or another manufacturinginput differed from like non-visual features of (many) other assemblyunits—of the same assembly type—produced on the assembly line (andlabeled as fully-functional or non-defective). Accordingly, the computersystem can implement the foregoing methods and techniques to: identifyassembly stages or manufacturing processes related to the non-visualanomaly; identify regions of interest or specific visual featureseffected within the assembly units during these assembly stages ormanufacturing processes based on the feature map; aggregate visualfeatures within these regions of interest into vectors or othercontainers for each assembly unit; derive correlations between theanomalous non-visual feature and these other visual features; isolateone or a subset of visual features exhibiting greatest correlation tothe anomalous non-visual feature; and return a prompt to the user toinspect assembly units with the anomalous non-visual feature in thecontext of the one or subset of visual features exhibiting greatestcorrelation to the anomalous non-visual feature. Therefore, in thisimplementation, the computer system can automatically isolate particularpoints, edges, corners, surfaces, or volumes within the assembly typethat may have been effected by a yet-uncharacterized manufacturing inputanomaly in some assembly units and then present both the uncharacterizedmanufacturing input anomaly and these particular points, edges, corners,surfaces, or volumes within the assembly type to the user, therebyenabling the user to quickly trace an effect of the manufacturing inputanomaly, determine whether the manufacturing input anomaly yields adefective assembly unit, and address changes at the assembly line todirectly reduce frequency of this manufacturing input anomaly.

However, the computer system can access a definition and related datafor a new defect in assembly units produced on the assembly line and/orisolate a new anomaly in a population of assembly units of this assemblytype in any other way. The computer system can also implement any othermethod and technique to identify this new defect or anomaly in otherassembly units produced on the assembly line.

The computer systems and methods described herein can be embodied and/orimplemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions can be executed by computer-executable componentsintegrated with the application, applet, host, server, network, website,communication service, communication interface,hardware/firmware/software elements of a user computer or mobile device,wristband, smartphone, or any suitable combination thereof. Othersystems and methods of the embodiment can be embodied and/or implementedat least in part as a machine configured to receive a computer-readablemedium storing computer-readable instructions. The instructions can beexecuted by computer-executable components integrated bycomputer-executable components integrated with apparatuses and networksof the type described above. The computer-readable medium can be storedon any suitable computer readable media such as RAMs, ROMs, flashmemory, EEPROMs, optical devices (CD or DVD), hard drives, floppydrives, or any suitable device. The computer-executable component can bea processor but any suitable dedicated hardware device can(alternatively or additionally) execute the instructions.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the embodiments of the invention without departing fromthe scope of this invention as defined in the following claims.

I claim:
 1. A method for monitoring manufacture of assembly unitscomprises: accessing a set of non-visual features for a set of assemblyunits, of a particular assembly type, during production of the set ofassembly units; linking the set of non-visual features to locationswithin assembly units of the particular assembly type; during a firsttime period: identifying a defect in a first subset of assembly units,in the set of assembly units of the particular assembly type; accessinga first inspection image, in a set of inspection images depicting thefirst subset of assembly units, recorded at an optical inspectionstation during production of the first subset of assembly units; andcalculating correlations between a first set of visual features in thefirst inspection image proximal a target location, a first subset ofnon-visual features associated with locations proximal the targetlocation, and the defect; and during a second time period following thefirst time period: isolating a particular non-visual feature, in the setof non-visual features, exhibiting correlation to the defect exceeding athreshold correlation; and generating a prompt to inspect a source ofthe particular non-visual feature.
 2. The method of claim 1: whereinlinking the set of non-visual features comprises accessing a feature maplinking the set of non-visual features to locations within assemblyunits of the particular assembly type; and further comprising, duringthe first time period: accessing the target location, for the particularassembly type, hypothesized by a user to contain an origin of thedefect; projecting the first target location onto the first inspectionimage; detecting the first set of visual features proximal the targetlocation in the inspection image; and based on the feature map,aggregating the first subset of non-visual features, in the set ofnon-visual features, associated with locations proximal the targetlocation.
 3. The method of claim 2, wherein aggregating the first subsetof non-visual features, in the set of non-visual features, associatedwith locations proximal the target location comprises: accessing atimestamp of the first inspection image; extracting a cluster ofnon-visual features, in the set of non-visual features, associated withtimes proximal the timestamp of the first inspection image; andextracting the first subset of non-visual features, associated withlocations proximal the target location for the assembly unit, from thecluster of non-visual features.
 4. The method of claim 1, whereinaccessing the set of non-visual features comprises accessing atimeseries of data selected from the set of input data, the time seriesof data comprising: a timeseries of manufacturing input data at anassembly line during production of the set of assembly units; atimeseries of environmental data recorded proximal the assembly line; atimeseries of identifiers of technicians present on the assembly line;and a timeseries of operations of tools on the assembly line.
 5. Themethod of claim 1: further comprising, during the first time period,accessing a target assembly stage, for the first subset of assemblyunits of the particular assembly type, hypothesized by the user tocontain the origin of the defect; and wherein accessing the firstinspection image, in the set of inspection images, comprises, accessingthe first inspection image recorded by the optical inspection stationupon conclusion of the target assembly stage for the set of assemblyunits.
 6. The method of claim 1: wherein linking the set of non-visualfeatures comprises accessing a feature map linking the set of non-visualfeatures to locations within assembly units of the particular assemblytype; and further comprising: accessing a set of production data for theparticular assembly type; interpreting a set of manufacturing steps forthe particular assembly type represented in the set of production data;associating each manufacturing step, in the set of manufacturing steps,with an assembly subregion, in a set of assembly subregions, of theparticular assembly type; associating each manufacturing step, in asubset of manufacturing steps, in the set of manufacturing steps, with aset of manufacturing input data streams, of the particular assemblytype; and compiling the set of manufacturing steps, the set of assemblysubregions, and the set of manufacturing input data streams into thefeature map for the particular assembly type.
 7. The method of claim 6,wherein associating each manufacturing step, in the set of manufacturingsteps, with the assembly region, in the set of assembly subregions, ofthe particular assembly type, comprises: recording a bounding box, drawnmanually over a representative inspection image of the particularassembly type, around a component added to the particular assembly typeduring the manufacturing step; defining the assembly subregion, in theset of assembly subregions, based on the bounding box; and writing apointer for the manufacturing step to the assembly subregion.
 8. Themethod of claim 6: wherein associating each manufacturing step, in theset of manufacturing steps, with the assembly region, in the set ofassembly subregions, of the particular assembly type, comprises:recording selection of a threaded fastener, depicted in a particularsubregion in a representative inspection image of the particularassembly type, installed on the particular assembly type during aparticular manufacturing step; and linking a particular subregion of theparticular assembly type containing the threaded fastener to theparticular manufacturing step; wherein associating each manufacturingstep, in the subset of manufacturing steps, in the set of manufacturingsteps, with the set of manufacturing input data streams, of theparticular assembly type comprises: accessing a particular data streamcontaining torque output values and torque dwell values output by ascrewdriver; and linking the particular data stream to the particularmanufacturing step; wherein compiling the set of manufacturing steps,the set of assembly subregions, and the set of manufacturing input datastreams into the feature map for the particular assembly type compriseslinking the particular data stream to the particular subregion of theparticular assembly type; and wherein calculating correlations betweenthe first set of visual features in the first inspection image proximalthe target location, the first subset of non-visual features associatedwith locations proximal the target location, and the defect comprisescalculating correlations between torque output values of thescrewdriver, torque dwell values of the screwdriver, and the defect forthe first subset of assembly units.
 9. The method of claim 1, whereingenerating a prompt to inspect a source of the particular non-visualfeature, comprises: isolating the first subset of non-visual features,from the set of non-visual features, exhibiting correlations to thedefect greater than the threshold correlation; isolating a first subsetof visual features, from the first set of visual features, proximal thetarget location in the first inspection image, exhibiting correlationsto the defect greater than the threshold correlation; aggregating thefirst subset of non-visual features and the first subset of visualfeatures into a feature list ranked by strength of correlation to thedefect; and serving the prompt to the user to investigate features inthe feature list for an origin of the defect.
 10. The method of Claim 9,wherein serving the prompt to the user to investigate features in thefeature list for the origin of the defect, comprises: retrieving arepresentative inspection image of the particular assembly type;rendering a heatmap depicting correlations of the first subset of visualfeatures over the representative inspection image; populating positionsover the representative inspection image corresponding to non-visualfeatures in the first subset of non-visual features with a set of flags,each flag in the set of flags linked to non-visual features in the firstsubset of non-visual features; and serving the representative inspectionimage to a user portal accessed at a computing device affiliated withthe user.
 11. method of claim 1, further comprising, during the firsttime period: accessing the target location, for the particular assemblytype, hypothesized by the user to contain an origin of the defectcomprising recording a bounding box, drawn manually over a subregion ofthe first inspection image of the particular assembly type, hypothesizedby the user to contain the origin of the defect; and extracting thefirst set of visual features from the subregion of the first inspectionimage at a feature density proportional to a quantity of assembly unitsin the set of assembly units.
 12. The method of claim 1, furthercomprising, in response to magnitudes of correlations between the firstset of visual features proximal the first target location, the firstsubset of non-visual features associated with locations proximal thefirst target location, and the defect remaining below the thresholdcorrelation: prompting the user to select a second target location inthe first inspection image, for the particular assembly type,hypothesized by the user to contain an alternate origin of the defect;and calculating a second set of correlations between a second set ofvisual features in the first inspection image proximal the second targetlocation, a second subset of non-visual features associated withlocations proximal the second target location, and the defect for theset of assembly units.
 13. The method of claim 1: further comprising,during the second time period following the first time period: accessinga second target location, for the particular assembly type, hypothesizedby the user to contain an origin of the defect, the second targetlocation offset and distinct from the first target location; andcalculating a second set of correlations between a second set of visualfeatures proximal the second target location, a second subset ofnon-visual features associated with locations proximal the second targetlocation, and the defect; and wherein generating the prompt to inspectthe source of the particular non-visual feature comprises: aggregatingthe first subset of non-visual features and the second subset ofnon-visual features into a feature list scored by strength ofcorrelation to the defect; and serving the prompt to the user toinvestigate features in the feature list for an origin of the defect.14. The method of claim 13: further comprising, during the first timeperiod, accessing the target location, for the particular assembly type,comprising recording a bounding box, drawn manually over a firstsubregion of a representative inspection image of the particularassembly type, hypothesized by the user to contain the origin of thedefect; and wherein accessing the second target location hypothesized bythe user to contain the origin of the defect comprises: in response toan area of the bounding box falling below a threshold area, promptingthe user to enter a second hypothesis for the origin of the defect; andrecording a second bounding box, drawn manually over a second subregionof the representative inspection image of the particular assembly type,hypothesized by the user to contain the origin of the defect.
 15. Themethod of claim 13, wherein accessing the second target locationhypothesized by the user to contain the origin of the defect comprises:in response to a quantity of assembly units in the set of assembly unitsexceeding a threshold quantity, prompting the user to enter a secondhypothesis for the origin of the defect; and recording a second boundingbox, drawn manually over a second subregion of the representativeinspection image of the particular assembly type, hypothesized by theuser to contain the origin of the defect.
 16. The method of claim 1,further comprising, in response to receipt of confirmation of theparticular non-visual manufacturing features as the origin of thedefect: scanning the set of non-visual features for a second subset ofassembly units, in the set of assembly units, associated with theparticular non-visual feature; and prompting the user to inspect thesecond subset of assembly units for the defect.
 17. The method of claim1: wherein isolating the particular non-visual feature comprisesisolating a particular input, in the set of inputs, linked to visualfeatures and non-visual features exhibiting greatest correlation to thedefect; and further comprising, during the second time period followingthe first time period, generating a prompt to inspect the particularinput for the origin of the defect.
 18. The method of claim 1: furthercomprising, during the first time period: calculating a weighted set ofvisual features, in the set of visual features, based on proximity tothe target location; and calculating a weighted set of non-visualfeatures, in the subset of non-visual features, based on proximity to aset of manufacturing steps associated with the target location for theparticular assembly type; wherein calculating correlations comprisescalculating correlations between the first set of visual features in thefirst inspection image proximal the target location, the first subset ofnon-visual features associated with locations proximal the targetlocation, and the defect, based on the weighted set of visual featuresand the weighted set of non-visual features; and further comprising,during the second time period following the first time period,predicting a particular input, in the set of inputs, linked to visualfeatures and non-visual features exhibiting greatest correlation to thedefect.
 19. A method for monitoring manufacture of assembly unitsincludes: during a first time period: accessing a first set ofnon-visual inputs representing a first set of inputs into a first subsetof assembly units, in a set of assembly units of a particular assemblytype, during production of the first subset of assembly units;identifying a defect in a first inspection image of a first assemblyunit in the first subset of assembly units; accessing a feature maplinking the defect in the first inspection image to the first set ofnon-visual features; calculating a weighted set of non-visual featuresbased on proximity of the first set of non-visual features to the defectin the first inspection image; and during a second time period followingthe first time period: accessing a second set of non-visual inputsrepresenting a second set of inputs into a second subset of assemblyunits, in the set of assembly units of the particular assembly type,during production of the second subset of assembly units; predicting aparticular non-visual feature, in the second set of non-visual features,representing a source of the defect based on comparing the weighted setof non-visual features to the second set of non-visual features; andgenerating a prompt suggesting a modification for a particular inputassociated with the particular non-visual feature.
 20. The method ofclaim 1, further comprising: during the first time period: extracting aset of visual features proximal a target location in the firstinspection image hypothesized by a user to contain the defect;calculating a weighted set of visual features based on proximity of theset of visual features to the defect identified in the first inspectionimage; and calculating correlations between the weighted set of visualfeatures proximal the target location, a subset of weighted non-visualfeatures associated with locations proximal the target location, and thedefect for first subset of assembly units; and during the second timeperiod following the first time period, isolating the particularnon-visual feature, in the second set of non-visual features, exhibitingcorrelation to the defect exceeding a threshold correlation.